Evidence aggregation networks for fuzzy logic inference

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Abstract
Fuzzy logic has been applied successfully in many engineering disciplines. In this paper, the problem of fuzzy logic inference is investigated as a question of aggregation of evidence. A fixed network architecture employing general fuzzy unions and intersections is proposed as a mechanism to implement fuzzy logic inference. It is shown that these networks possess desirable theoretical properties. Networks based on parameterized families of operators (such as Yager's union and intersection) have extra predictable properties and admit a training algorithm which produces sharper inference results than were earlier obtained. Simulation studies are presented which corroborate the theoretical properties.